18 citations · 81 across the 17 of their papers we have counts for
19 papers
Robust RF Data Normalization for Deep Learning
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted…
Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultra…
Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach
Mostafa Sharifzadeh, Sobhan Goudarzi, An Tang +2
One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which…
Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium. Th…
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor +1
The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising perf…
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images
Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community. However, the performa…